Overview

Dataset statistics

Number of variables45
Number of observations43592
Missing cells3764
Missing cells (%)0.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory15.0 MiB
Average record size in memory360.0 B

Variable types

Numeric11
Categorical17
Boolean17

Alerts

height is highly overall correlated with engine volume and 34 other fieldsHigh correlation
engine volume is highly overall correlated with height and 33 other fieldsHigh correlation
population is highly overall correlated with cluster areaHigh correlation
length is highly overall correlated with height and 33 other fieldsHigh correlation
width is highly overall correlated with height and 34 other fieldsHigh correlation
gross weight is highly overall correlated with height and 34 other fieldsHigh correlation
turning radius is highly overall correlated with height and 35 other fieldsHigh correlation
ncap rating is highly overall correlated with height and 32 other fieldsHigh correlation
is power door locks is highly overall correlated with height and 20 other fieldsHigh correlation
is parking camera is highly overall correlated with height and 23 other fieldsHigh correlation
rear brakes type is highly overall correlated with height and 23 other fieldsHigh correlation
is adjustable steering is highly overall correlated with height and 26 other fieldsHigh correlation
is tpms is highly overall correlated with height and 23 other fieldsHigh correlation
is driver seat height adjustable is highly overall correlated with height and 26 other fieldsHigh correlation
segment is highly overall correlated with height and 33 other fieldsHigh correlation
is central locking is highly overall correlated with height and 20 other fieldsHigh correlation
is rear window wiper is highly overall correlated with height and 26 other fieldsHigh correlation
cluster area is highly overall correlated with populationHigh correlation
is ecw is highly overall correlated with height and 20 other fieldsHigh correlation
fuel type is highly overall correlated with height and 32 other fieldsHigh correlation
torque is highly overall correlated with height and 35 other fieldsHigh correlation
transmission type is highly overall correlated with height and 22 other fieldsHigh correlation
manufacturer is highly overall correlated with height and 29 other fieldsHigh correlation
cylinder is highly overall correlated with height and 22 other fieldsHigh correlation
is rear window washer is highly overall correlated with height and 26 other fieldsHigh correlation
is front fog lights is highly overall correlated with height and 25 other fieldsHigh correlation
is brake assist is highly overall correlated with height and 28 other fieldsHigh correlation
is power steering is highly overall correlated with height and 10 other fieldsHigh correlation
is esc is highly overall correlated with height and 26 other fieldsHigh correlation
is rear window defogger is highly overall correlated with height and 27 other fieldsHigh correlation
engine type is highly overall correlated with height and 35 other fieldsHigh correlation
is speed alert is highly overall correlated with height and 5 other fieldsHigh correlation
steering type is highly overall correlated with height and 19 other fieldsHigh correlation
is parking sensors is highly overall correlated with engine volume and 9 other fieldsHigh correlation
power is highly overall correlated with height and 35 other fieldsHigh correlation
is day night rear view mirror is highly overall correlated with height and 14 other fieldsHigh correlation
model is highly overall correlated with height and 35 other fieldsHigh correlation
gear box is highly overall correlated with height and 23 other fieldsHigh correlation
airbags is highly overall correlated with height and 29 other fieldsHigh correlation
is power steering is highly imbalanced (85.5%)Imbalance
is speed alert is highly imbalanced (94.6%)Imbalance
is parking sensors is highly imbalanced (75.2%)Imbalance
is claim is highly imbalanced (65.7%)Imbalance
ID is uniformly distributedUniform
ID has unique valuesUnique
car age has 3854 (8.8%) zerosZeros

Reproduction

Analysis started2023-01-05 08:29:12.904228
Analysis finished2023-01-05 08:29:42.182334
Duration29.28 seconds
Software versionpandas-profiling vv3.6.2
Download configurationconfig.json

Variables

ID
Real number (ℝ)

UNIFORM  UNIQUE 

Distinct43592
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21795.5
Minimum0
Maximum43591
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size340.7 KiB
2023-01-05T11:29:42.262995image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2179.55
Q110897.75
median21795.5
Q332693.25
95-th percentile41411.45
Maximum43591
Range43591
Interquartile range (IQR)21795.5

Descriptive statistics

Standard deviation12584.071
Coefficient of variation (CV)0.57737014
Kurtosis-1.2
Mean21795.5
Median Absolute Deviation (MAD)10898
Skewness0
Sum9.5010944 × 108
Variance1.5835884 × 108
MonotonicityStrictly increasing
2023-01-05T11:29:42.378297image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1
 
< 0.1%
29064 1
 
< 0.1%
29056 1
 
< 0.1%
29057 1
 
< 0.1%
29058 1
 
< 0.1%
29059 1
 
< 0.1%
29060 1
 
< 0.1%
29061 1
 
< 0.1%
29062 1
 
< 0.1%
29063 1
 
< 0.1%
Other values (43582) 43582
> 99.9%
ValueCountFrequency (%)
0 1
< 0.1%
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
ValueCountFrequency (%)
43591 1
< 0.1%
43590 1
< 0.1%
43589 1
< 0.1%
43588 1
< 0.1%
43587 1
< 0.1%
43586 1
< 0.1%
43585 1
< 0.1%
43584 1
< 0.1%
43583 1
< 0.1%
43582 1
< 0.1%

ncap rating
Categorical

Distinct5
Distinct (%)< 0.1%
Missing99
Missing (%)0.2%
Memory size340.7 KiB
2.0
15922 
0.0
14083 
3.0
10462 
4.0
 
1572
5.0
 
1454

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters130479
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row2.0
4th row0.0
5th row3.0

Common Values

ValueCountFrequency (%)
2.0 15922
36.5%
0.0 14083
32.3%
3.0 10462
24.0%
4.0 1572
 
3.6%
5.0 1454
 
3.3%
(Missing) 99
 
0.2%

Length

2023-01-05T11:29:42.468768image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-05T11:29:42.567832image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
2.0 15922
36.6%
0.0 14083
32.4%
3.0 10462
24.1%
4.0 1572
 
3.6%
5.0 1454
 
3.3%

Most occurring characters

ValueCountFrequency (%)
0 57576
44.1%
. 43493
33.3%
2 15922
 
12.2%
3 10462
 
8.0%
4 1572
 
1.2%
5 1454
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 86986
66.7%
Other Punctuation 43493
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 57576
66.2%
2 15922
 
18.3%
3 10462
 
12.0%
4 1572
 
1.8%
5 1454
 
1.7%
Other Punctuation
ValueCountFrequency (%)
. 43493
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 130479
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 57576
44.1%
. 43493
33.3%
2 15922
 
12.2%
3 10462
 
8.0%
4 1572
 
1.2%
5 1454
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 130479
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 57576
44.1%
. 43493
33.3%
2 15922
 
12.2%
3 10462
 
8.0%
4 1572
 
1.2%
5 1454
 
1.1%
Distinct2
Distinct (%)< 0.1%
Missing94
Missing (%)0.2%
Memory size85.3 KiB
True
31642 
False
11856 
(Missing)
 
94
ValueCountFrequency (%)
True 31642
72.6%
False 11856
 
27.2%
(Missing) 94
 
0.2%
2023-01-05T11:29:42.655600image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

policyholder age
Real number (ℝ)

Distinct43511
Distinct (%)100.0%
Missing81
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean0.57978455
Minimum0.38347717
Maximum1.1122162
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size340.7 KiB
2023-01-05T11:29:42.736786image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.38347717
5-th percentile0.4128067
Q10.48012905
median0.56398692
Q30.65672146
95-th percentile0.81521786
Maximum1.1122162
Range0.72873898
Interquartile range (IQR)0.17659241

Descriptive statistics

Standard deviation0.12312679
Coefficient of variation (CV)0.21236646
Kurtosis-0.15372984
Mean0.57978455
Median Absolute Deviation (MAD)0.087730781
Skewness0.63871836
Sum25227.005
Variance0.015160207
MonotonicityNot monotonic
2023-01-05T11:29:42.837752image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.394496401 1
 
< 0.1%
0.4566026343 1
 
< 0.1%
0.464422113 1
 
< 0.1%
0.6884120207 1
 
< 0.1%
0.6733664485 1
 
< 0.1%
0.4203120039 1
 
< 0.1%
0.5598338848 1
 
< 0.1%
0.4262910224 1
 
< 0.1%
0.5413385843 1
 
< 0.1%
0.567988909 1
 
< 0.1%
Other values (43501) 43501
99.8%
(Missing) 81
 
0.2%
ValueCountFrequency (%)
0.3834771727 1
< 0.1%
0.3875241064 1
< 0.1%
0.3875285596 1
< 0.1%
0.388134091 1
< 0.1%
0.3884033162 1
< 0.1%
0.3886124568 1
< 0.1%
0.388654659 1
< 0.1%
0.3888216761 1
< 0.1%
0.3890545244 1
< 0.1%
0.3893033079 1
< 0.1%
ValueCountFrequency (%)
1.112216153 1
< 0.1%
1.094787636 1
< 0.1%
1.075792231 1
< 0.1%
1.07001303 1
< 0.1%
1.068993542 1
< 0.1%
1.064476344 1
< 0.1%
1.063323288 1
< 0.1%
1.06124758 1
< 0.1%
1.059003483 1
< 0.1%
1.055541967 1
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing83
Missing (%)0.2%
Memory size85.3 KiB
False
26396 
True
17113 
(Missing)
 
83
ValueCountFrequency (%)
False 26396
60.6%
True 17113
39.3%
(Missing) 83
 
0.2%
2023-01-05T11:29:42.939706image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

rear brakes type
Categorical

Distinct2
Distinct (%)< 0.1%
Missing86
Missing (%)0.2%
Memory size340.7 KiB
Drum
33042 
Disc
10464 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters174024
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDrum
2nd rowDrum
3rd rowDrum
4th rowDrum
5th rowDisc

Common Values

ValueCountFrequency (%)
Drum 33042
75.8%
Disc 10464
 
24.0%
(Missing) 86
 
0.2%

Length

2023-01-05T11:29:43.011147image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-05T11:29:43.088514image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
drum 33042
75.9%
disc 10464
 
24.1%

Most occurring characters

ValueCountFrequency (%)
D 43506
25.0%
r 33042
19.0%
u 33042
19.0%
m 33042
19.0%
i 10464
 
6.0%
s 10464
 
6.0%
c 10464
 
6.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 130518
75.0%
Uppercase Letter 43506
 
25.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 33042
25.3%
u 33042
25.3%
m 33042
25.3%
i 10464
 
8.0%
s 10464
 
8.0%
c 10464
 
8.0%
Uppercase Letter
ValueCountFrequency (%)
D 43506
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 174024
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
D 43506
25.0%
r 33042
19.0%
u 33042
19.0%
m 33042
19.0%
i 10464
 
6.0%
s 10464
 
6.0%
c 10464
 
6.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 174024
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
D 43506
25.0%
r 33042
19.0%
u 33042
19.0%
m 33042
19.0%
i 10464
 
6.0%
s 10464
 
6.0%
c 10464
 
6.0%
Distinct2
Distinct (%)< 0.1%
Missing90
Missing (%)0.2%
Memory size85.3 KiB
True
26492 
False
17010 
(Missing)
 
90
ValueCountFrequency (%)
True 26492
60.8%
False 17010
39.0%
(Missing) 90
 
0.2%
2023-01-05T11:29:43.164339image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

is tpms
Boolean

Distinct2
Distinct (%)< 0.1%
Missing89
Missing (%)0.2%
Memory size85.3 KiB
False
33036 
True
10467 
(Missing)
 
89
ValueCountFrequency (%)
False 33036
75.8%
True 10467
 
24.0%
(Missing) 89
 
0.2%
2023-01-05T11:29:43.239241image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing85
Missing (%)0.2%
Memory size85.3 KiB
True
25570 
False
17937 
(Missing)
 
85
ValueCountFrequency (%)
True 25570
58.7%
False 17937
41.1%
(Missing) 85
 
0.2%
2023-01-05T11:29:43.314297image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

segment
Categorical

Distinct6
Distinct (%)< 0.1%
Missing83
Missing (%)0.2%
Memory size340.7 KiB
B2
13661 
A
12766 
C2
10465 
B1
3088 
C1
2631 

Length

Max length7
Median length2
Mean length1.8097865
Min length1

Characters and Unicode

Total characters78742
Distinct characters10
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowB1
2nd rowB1
3rd rowB2
4th rowA
5th rowC2

Common Values

ValueCountFrequency (%)
B2 13661
31.3%
A 12766
29.3%
C2 10465
24.0%
B1 3088
 
7.1%
C1 2631
 
6.0%
Utility 898
 
2.1%
(Missing) 83
 
0.2%

Length

2023-01-05T11:29:43.384898image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-05T11:29:43.472986image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
b2 13661
31.4%
a 12766
29.3%
c2 10465
24.1%
b1 3088
 
7.1%
c1 2631
 
6.0%
utility 898
 
2.1%

Most occurring characters

ValueCountFrequency (%)
2 24126
30.6%
B 16749
21.3%
C 13096
16.6%
A 12766
16.2%
1 5719
 
7.3%
t 1796
 
2.3%
i 1796
 
2.3%
U 898
 
1.1%
l 898
 
1.1%
y 898
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 43509
55.3%
Decimal Number 29845
37.9%
Lowercase Letter 5388
 
6.8%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
B 16749
38.5%
C 13096
30.1%
A 12766
29.3%
U 898
 
2.1%
Lowercase Letter
ValueCountFrequency (%)
t 1796
33.3%
i 1796
33.3%
l 898
16.7%
y 898
16.7%
Decimal Number
ValueCountFrequency (%)
2 24126
80.8%
1 5719
 
19.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 48897
62.1%
Common 29845
37.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
B 16749
34.3%
C 13096
26.8%
A 12766
26.1%
t 1796
 
3.7%
i 1796
 
3.7%
U 898
 
1.8%
l 898
 
1.8%
y 898
 
1.8%
Common
ValueCountFrequency (%)
2 24126
80.8%
1 5719
 
19.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 78742
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 24126
30.6%
B 16749
21.3%
C 13096
16.6%
A 12766
16.2%
1 5719
 
7.3%
t 1796
 
2.3%
i 1796
 
2.3%
U 898
 
1.1%
l 898
 
1.1%
y 898
 
1.1%

car age
Real number (ℝ)

Distinct48
Distinct (%)0.1%
Missing105
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean0.069611608
Minimum0
Maximum1
Zeros3854
Zeros (%)8.8%
Negative0
Negative (%)0.0%
Memory size340.7 KiB
2023-01-05T11:29:43.567799image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.02
median0.06
Q30.11
95-th percentile0.17
Maximum1
Range1
Interquartile range (IQR)0.09

Descriptive statistics

Standard deviation0.05706333
Coefficient of variation (CV)0.81973871
Kurtosis6.7689025
Mean0.069611608
Median Absolute Deviation (MAD)0.04
Skewness1.1863893
Sum3027.2
Variance0.0032562236
MonotonicityNot monotonic
2023-01-05T11:29:43.668369image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
0.01 4752
 
10.9%
0.02 3900
 
8.9%
0 3854
 
8.8%
0.03 3267
 
7.5%
0.04 2755
 
6.3%
0.05 2448
 
5.6%
0.1 2306
 
5.3%
0.06 2240
 
5.1%
0.07 2185
 
5.0%
0.08 2008
 
4.6%
Other values (38) 13772
31.6%
ValueCountFrequency (%)
0 3854
8.8%
0.01 4752
10.9%
0.02 3900
8.9%
0.03 3267
7.5%
0.04 2755
6.3%
0.05 2448
5.6%
0.06 2240
5.1%
0.07 2185
5.0%
0.08 2008
4.6%
0.09 1978
4.5%
ValueCountFrequency (%)
1 3
< 0.1%
0.82 1
 
< 0.1%
0.81 1
 
< 0.1%
0.62 1
 
< 0.1%
0.49 1
 
< 0.1%
0.46 1
 
< 0.1%
0.45 1
 
< 0.1%
0.44 2
 
< 0.1%
0.42 1
 
< 0.1%
0.39 5
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing89
Missing (%)0.2%
Memory size85.3 KiB
True
31634 
False
11869 
(Missing)
 
89
ValueCountFrequency (%)
True 31634
72.6%
False 11869
 
27.2%
(Missing) 89
 
0.2%
2023-01-05T11:29:43.768634image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing69
Missing (%)0.2%
Memory size85.3 KiB
False
30836 
True
12687 
(Missing)
 
69
ValueCountFrequency (%)
False 30836
70.7%
True 12687
29.1%
(Missing) 69
 
0.2%
2023-01-05T11:29:43.847324image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

height
Real number (ℝ)

Distinct11
Distinct (%)< 0.1%
Missing96
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean1553.528
Minimum1475
Maximum1825
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size340.7 KiB
2023-01-05T11:29:43.917325image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1475
5-th percentile1475
Q11475
median1530
Q31635
95-th percentile1675
Maximum1825
Range350
Interquartile range (IQR)160

Descriptive statistics

Standard deviation79.579923
Coefficient of variation (CV)0.05122529
Kurtosis0.72618418
Mean1553.528
Median Absolute Deviation (MAD)55
Skewness1.0336072
Sum67572254
Variance6332.9641
MonotonicityNot monotonic
2023-01-05T11:29:43.989585image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
1475 10968
25.2%
1635 10456
24.0%
1530 10254
23.5%
1675 3085
 
7.1%
1500 2217
 
5.1%
1490 1797
 
4.1%
1501 1573
 
3.6%
1523 1191
 
2.7%
1825 899
 
2.1%
1515 788
 
1.8%
ValueCountFrequency (%)
1475 10968
25.2%
1490 1797
 
4.1%
1500 2217
 
5.1%
1501 1573
 
3.6%
1515 788
 
1.8%
1523 1191
 
2.7%
1530 10254
23.5%
1606 268
 
0.6%
1635 10456
24.0%
1675 3085
 
7.1%
ValueCountFrequency (%)
1825 899
 
2.1%
1675 3085
 
7.1%
1635 10456
24.0%
1606 268
 
0.6%
1530 10254
23.5%
1523 1191
 
2.7%
1515 788
 
1.8%
1501 1573
 
3.6%
1500 2217
 
5.1%
1490 1797
 
4.1%

cluster area
Categorical

Distinct22
Distinct (%)0.1%
Missing85
Missing (%)0.2%
Memory size340.7 KiB
Area_8
10198 
Area_2
5509 
Area_5
5182 
Area_3
4457 
Area_14
2734 
Other values (17)
15427 

Length

Max length7
Median length6
Mean length6.2823684
Min length6

Characters and Unicode

Total characters273327
Distinct characters15
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowArea_8
2nd rowArea_5
3rd rowArea_2
4th rowArea_13
5th rowArea_9

Common Values

ValueCountFrequency (%)
Area_8 10198
23.4%
Area_2 5509
12.6%
Area_5 5182
11.9%
Area_3 4457
10.2%
Area_14 2734
 
6.3%
Area_13 2521
 
5.8%
Area_10 2343
 
5.4%
Area_9 2065
 
4.7%
Area_7 1579
 
3.6%
Area_12 1159
 
2.7%
Other values (12) 5760
13.2%

Length

2023-01-05T11:29:44.087268image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
area_8 10198
23.4%
area_2 5509
12.7%
area_5 5182
11.9%
area_3 4457
10.2%
area_14 2734
 
6.3%
area_13 2521
 
5.8%
area_10 2343
 
5.4%
area_9 2065
 
4.7%
area_7 1579
 
3.6%
area_12 1159
 
2.7%
Other values (12) 5760
13.2%

Most occurring characters

ValueCountFrequency (%)
A 43507
15.9%
r 43507
15.9%
e 43507
15.9%
a 43507
15.9%
_ 43507
15.9%
1 14051
 
5.1%
8 10373
 
3.8%
2 7333
 
2.7%
3 6978
 
2.6%
5 5745
 
2.1%
Other values (5) 11312
 
4.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 130521
47.8%
Decimal Number 55792
20.4%
Uppercase Letter 43507
 
15.9%
Connector Punctuation 43507
 
15.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 14051
25.2%
8 10373
18.6%
2 7333
13.1%
3 6978
12.5%
5 5745
10.3%
4 3207
 
5.7%
9 2761
 
4.9%
0 2421
 
4.3%
7 1960
 
3.5%
6 963
 
1.7%
Lowercase Letter
ValueCountFrequency (%)
r 43507
33.3%
e 43507
33.3%
a 43507
33.3%
Uppercase Letter
ValueCountFrequency (%)
A 43507
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 43507
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 174028
63.7%
Common 99299
36.3%

Most frequent character per script

Common
ValueCountFrequency (%)
_ 43507
43.8%
1 14051
 
14.2%
8 10373
 
10.4%
2 7333
 
7.4%
3 6978
 
7.0%
5 5745
 
5.8%
4 3207
 
3.2%
9 2761
 
2.8%
0 2421
 
2.4%
7 1960
 
2.0%
Latin
ValueCountFrequency (%)
A 43507
25.0%
r 43507
25.0%
e 43507
25.0%
a 43507
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 273327
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 43507
15.9%
r 43507
15.9%
e 43507
15.9%
a 43507
15.9%
_ 43507
15.9%
1 14051
 
5.1%
8 10373
 
3.8%
2 7333
 
2.7%
3 6978
 
2.6%
5 5745
 
2.1%
Other values (5) 11312
 
4.1%

is ecw
Boolean

Distinct2
Distinct (%)< 0.1%
Missing85
Missing (%)0.2%
Memory size85.3 KiB
True
31635 
False
11872 
(Missing)
 
85
ValueCountFrequency (%)
True 31635
72.6%
False 11872
 
27.2%
(Missing) 85
 
0.2%
2023-01-05T11:29:44.175920image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

fuel type
Categorical

Distinct3
Distinct (%)< 0.1%
Missing89
Missing (%)0.2%
Memory size340.7 KiB
Petrol
15320 
CNG
14952 
Diesel
13231 

Length

Max length6
Median length6
Mean length4.9688987
Min length3

Characters and Unicode

Total characters216162
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCNG
2nd rowCNG
3rd rowPetrol
4th rowCNG
5th rowDiesel

Common Values

ValueCountFrequency (%)
Petrol 15320
35.1%
CNG 14952
34.3%
Diesel 13231
30.4%
(Missing) 89
 
0.2%

Length

2023-01-05T11:29:44.246650image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-05T11:29:44.357634image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
petrol 15320
35.2%
cng 14952
34.4%
diesel 13231
30.4%

Most occurring characters

ValueCountFrequency (%)
e 41782
19.3%
l 28551
13.2%
P 15320
 
7.1%
t 15320
 
7.1%
r 15320
 
7.1%
o 15320
 
7.1%
C 14952
 
6.9%
N 14952
 
6.9%
G 14952
 
6.9%
D 13231
 
6.1%
Other values (2) 26462
12.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 142755
66.0%
Uppercase Letter 73407
34.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 41782
29.3%
l 28551
20.0%
t 15320
 
10.7%
r 15320
 
10.7%
o 15320
 
10.7%
i 13231
 
9.3%
s 13231
 
9.3%
Uppercase Letter
ValueCountFrequency (%)
P 15320
20.9%
C 14952
20.4%
N 14952
20.4%
G 14952
20.4%
D 13231
18.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 216162
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 41782
19.3%
l 28551
13.2%
P 15320
 
7.1%
t 15320
 
7.1%
r 15320
 
7.1%
o 15320
 
7.1%
C 14952
 
6.9%
N 14952
 
6.9%
G 14952
 
6.9%
D 13231
 
6.1%
Other values (2) 26462
12.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 216162
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 41782
19.3%
l 28551
13.2%
P 15320
 
7.1%
t 15320
 
7.1%
r 15320
 
7.1%
o 15320
 
7.1%
C 14952
 
6.9%
N 14952
 
6.9%
G 14952
 
6.9%
D 13231
 
6.1%
Other values (2) 26462
12.2%

torque
Categorical

Distinct9
Distinct (%)< 0.1%
Missing76
Missing (%)0.2%
Memory size340.7 KiB
113Nm@4400rpm
13260 
60Nm@3500rpm
10970 
250Nm@2750rpm
10470 
82.1Nm@3400rpm
3089 
91Nm@4250rpm
1795 
Other values (4)
3932 

Length

Max length14
Median length13
Mean length12.757009
Min length12

Characters and Unicode

Total characters555134
Distinct characters16
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row82.1Nm@3400rpm
2nd row82.1Nm@3400rpm
3rd row113Nm@4400rpm
4th row60Nm@3500rpm
5th row250Nm@2750rpm

Common Values

ValueCountFrequency (%)
113Nm@4400rpm 13260
30.4%
60Nm@3500rpm 10970
25.2%
250Nm@2750rpm 10470
24.0%
82.1Nm@3400rpm 3089
 
7.1%
91Nm@4250rpm 1795
 
4.1%
200Nm@1750rpm 1575
 
3.6%
200Nm@3000rpm 1190
 
2.7%
85Nm@3000rpm 898
 
2.1%
170Nm@4000rpm 269
 
0.6%
(Missing) 76
 
0.2%

Length

2023-01-05T11:29:44.438865image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-05T11:29:44.537632image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
113nm@4400rpm 13260
30.5%
60nm@3500rpm 10970
25.2%
250nm@2750rpm 10470
24.1%
82.1nm@3400rpm 3089
 
7.1%
91nm@4250rpm 1795
 
4.1%
200nm@1750rpm 1575
 
3.6%
200nm@3000rpm 1190
 
2.7%
85nm@3000rpm 898
 
2.1%
170nm@4000rpm 269
 
0.6%

Most occurring characters

ValueCountFrequency (%)
0 102788
18.5%
m 87032
15.7%
N 43516
7.8%
@ 43516
7.8%
r 43516
7.8%
p 43516
7.8%
5 36178
 
6.5%
1 33248
 
6.0%
4 31673
 
5.7%
3 29407
 
5.3%
Other values (6) 60744
10.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 290949
52.4%
Lowercase Letter 174064
31.4%
Other Punctuation 46605
 
8.4%
Uppercase Letter 43516
 
7.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 102788
35.3%
5 36178
 
12.4%
1 33248
 
11.4%
4 31673
 
10.9%
3 29407
 
10.1%
2 28589
 
9.8%
7 12314
 
4.2%
6 10970
 
3.8%
8 3987
 
1.4%
9 1795
 
0.6%
Lowercase Letter
ValueCountFrequency (%)
m 87032
50.0%
r 43516
25.0%
p 43516
25.0%
Other Punctuation
ValueCountFrequency (%)
@ 43516
93.4%
. 3089
 
6.6%
Uppercase Letter
ValueCountFrequency (%)
N 43516
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 337554
60.8%
Latin 217580
39.2%

Most frequent character per script

Common
ValueCountFrequency (%)
0 102788
30.5%
@ 43516
12.9%
5 36178
 
10.7%
1 33248
 
9.8%
4 31673
 
9.4%
3 29407
 
8.7%
2 28589
 
8.5%
7 12314
 
3.6%
6 10970
 
3.2%
8 3987
 
1.2%
Other values (2) 4884
 
1.4%
Latin
ValueCountFrequency (%)
m 87032
40.0%
N 43516
20.0%
r 43516
20.0%
p 43516
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 555134
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 102788
18.5%
m 87032
15.7%
N 43516
7.8%
@ 43516
7.8%
r 43516
7.8%
p 43516
7.8%
5 36178
 
6.5%
1 33248
 
6.0%
4 31673
 
5.7%
3 29407
 
5.3%
Other values (6) 60744
10.9%

engine volume
Real number (ℝ)

Distinct9
Distinct (%)< 0.1%
Missing103
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean1163.9933
Minimum796
Maximum1498
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size340.7 KiB
2023-01-05T11:29:44.624744image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum796
5-th percentile796
Q1796
median1197
Q31493
95-th percentile1497
Maximum1498
Range702
Interquartile range (IQR)697

Descriptive statistics

Standard deviation265.87761
Coefficient of variation (CV)0.22841851
Kurtosis-1.336397
Mean1163.9933
Median Absolute Deviation (MAD)296
Skewness-0.11307557
Sum50620903
Variance70690.903
MonotonicityNot monotonic
2023-01-05T11:29:44.696408image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1197 13251
30.4%
796 10959
25.1%
1493 10466
24.0%
998 3084
 
7.1%
999 1797
 
4.1%
1498 1575
 
3.6%
1497 1190
 
2.7%
1196 898
 
2.1%
1199 269
 
0.6%
(Missing) 103
 
0.2%
ValueCountFrequency (%)
796 10959
25.1%
998 3084
 
7.1%
999 1797
 
4.1%
1196 898
 
2.1%
1197 13251
30.4%
1199 269
 
0.6%
1493 10466
24.0%
1497 1190
 
2.7%
1498 1575
 
3.6%
ValueCountFrequency (%)
1498 1575
 
3.6%
1497 1190
 
2.7%
1493 10466
24.0%
1199 269
 
0.6%
1197 13251
30.4%
1196 898
 
2.1%
999 1797
 
4.1%
998 3084
 
7.1%
796 10959
25.1%
Distinct2
Distinct (%)< 0.1%
Missing97
Missing (%)0.2%
Memory size340.7 KiB
Manual
28235 
Automatic
15260 

Length

Max length9
Median length6
Mean length7.0525348
Min length6

Characters and Unicode

Total characters306750
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowManual
2nd rowManual
3rd rowManual
4th rowManual
5th rowAutomatic

Common Values

ValueCountFrequency (%)
Manual 28235
64.8%
Automatic 15260
35.0%
(Missing) 97
 
0.2%

Length

2023-01-05T11:29:44.798524image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-05T11:29:44.881949image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
manual 28235
64.9%
automatic 15260
35.1%

Most occurring characters

ValueCountFrequency (%)
a 71730
23.4%
u 43495
14.2%
t 30520
9.9%
M 28235
 
9.2%
n 28235
 
9.2%
l 28235
 
9.2%
A 15260
 
5.0%
o 15260
 
5.0%
m 15260
 
5.0%
i 15260
 
5.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 263255
85.8%
Uppercase Letter 43495
 
14.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 71730
27.2%
u 43495
16.5%
t 30520
11.6%
n 28235
 
10.7%
l 28235
 
10.7%
o 15260
 
5.8%
m 15260
 
5.8%
i 15260
 
5.8%
c 15260
 
5.8%
Uppercase Letter
ValueCountFrequency (%)
M 28235
64.9%
A 15260
35.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 306750
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 71730
23.4%
u 43495
14.2%
t 30520
9.9%
M 28235
 
9.2%
n 28235
 
9.2%
l 28235
 
9.2%
A 15260
 
5.0%
o 15260
 
5.0%
m 15260
 
5.0%
i 15260
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 306750
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 71730
23.4%
u 43495
14.2%
t 30520
9.9%
M 28235
 
9.2%
n 28235
 
9.2%
l 28235
 
9.2%
A 15260
 
5.0%
o 15260
 
5.0%
m 15260
 
5.0%
i 15260
 
5.0%

manufacturer
Categorical

Distinct5
Distinct (%)< 0.1%
Missing87
Missing (%)0.2%
Memory size340.7 KiB
1.0
28214 
3.0
10465 
2.0
 
1793
5.0
 
1576
4.0
 
1457

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters130515
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row3.0

Common Values

ValueCountFrequency (%)
1.0 28214
64.7%
3.0 10465
 
24.0%
2.0 1793
 
4.1%
5.0 1576
 
3.6%
4.0 1457
 
3.3%
(Missing) 87
 
0.2%

Length

2023-01-05T11:29:44.945494image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-05T11:29:45.022955image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 28214
64.9%
3.0 10465
 
24.1%
2.0 1793
 
4.1%
5.0 1576
 
3.6%
4.0 1457
 
3.3%

Most occurring characters

ValueCountFrequency (%)
. 43505
33.3%
0 43505
33.3%
1 28214
21.6%
3 10465
 
8.0%
2 1793
 
1.4%
5 1576
 
1.2%
4 1457
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 87010
66.7%
Other Punctuation 43505
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 43505
50.0%
1 28214
32.4%
3 10465
 
12.0%
2 1793
 
2.1%
5 1576
 
1.8%
4 1457
 
1.7%
Other Punctuation
ValueCountFrequency (%)
. 43505
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 130515
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 43505
33.3%
0 43505
33.3%
1 28214
21.6%
3 10465
 
8.0%
2 1793
 
1.4%
5 1576
 
1.2%
4 1457
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 130515
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 43505
33.3%
0 43505
33.3%
1 28214
21.6%
3 10465
 
8.0%
2 1793
 
1.4%
5 1576
 
1.2%
4 1457
 
1.1%

cylinder
Categorical

Distinct2
Distinct (%)< 0.1%
Missing97
Missing (%)0.2%
Memory size340.7 KiB
4.0
27383 
3.0
16112 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters130485
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row3.0
3rd row4.0
4th row3.0
5th row4.0

Common Values

ValueCountFrequency (%)
4.0 27383
62.8%
3.0 16112
37.0%
(Missing) 97
 
0.2%

Length

2023-01-05T11:29:45.092479image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-05T11:29:45.164805image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
4.0 27383
63.0%
3.0 16112
37.0%

Most occurring characters

ValueCountFrequency (%)
. 43495
33.3%
0 43495
33.3%
4 27383
21.0%
3 16112
 
12.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 86990
66.7%
Other Punctuation 43495
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 43495
50.0%
4 27383
31.5%
3 16112
 
18.5%
Other Punctuation
ValueCountFrequency (%)
. 43495
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 130485
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 43495
33.3%
0 43495
33.3%
4 27383
21.0%
3 16112
 
12.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 130485
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 43495
33.3%
0 43495
33.3%
4 27383
21.0%
3 16112
 
12.3%
Distinct2
Distinct (%)< 0.1%
Missing89
Missing (%)0.2%
Memory size85.3 KiB
False
30822 
True
12681 
(Missing)
 
89
ValueCountFrequency (%)
False 30822
70.7%
True 12681
29.1%
(Missing) 89
 
0.2%
2023-01-05T11:29:45.234944image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing86
Missing (%)0.2%
Memory size85.3 KiB
True
25297 
False
18209 
(Missing)
 
86
ValueCountFrequency (%)
True 25297
58.0%
False 18209
41.8%
(Missing) 86
 
0.2%
2023-01-05T11:29:45.304880image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing105
Missing (%)0.2%
Memory size85.3 KiB
True
23983 
False
19504 
(Missing)
 
105
ValueCountFrequency (%)
True 23983
55.0%
False 19504
44.7%
(Missing) 105
 
0.2%
2023-01-05T11:29:45.375188image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

is power steering
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing92
Missing (%)0.2%
Memory size85.3 KiB
True
42605 
False
 
895
(Missing)
 
92
ValueCountFrequency (%)
True 42605
97.7%
False 895
 
2.1%
(Missing) 92
 
0.2%
2023-01-05T11:29:45.458745image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

is esc
Boolean

Distinct2
Distinct (%)< 0.1%
Missing78
Missing (%)0.2%
Memory size85.3 KiB
False
29771 
True
13743 
(Missing)
 
78
ValueCountFrequency (%)
False 29771
68.3%
True 13743
31.5%
(Missing) 78
 
0.2%
2023-01-05T11:29:45.545831image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

population
Real number (ℝ)

Distinct1949
Distinct (%)4.5%
Missing95
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean18913.13
Minimum291
Maximum73529
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size340.7 KiB
2023-01-05T11:29:45.626139image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum291
5-th percentile4107
Q16190
median8869
Q327086
95-th percentile73438
Maximum73529
Range73238
Interquartile range (IQR)20896

Descriptive statistics

Standard deviation17667.349
Coefficient of variation (CV)0.93413139
Kurtosis2.5827368
Mean18913.13
Median Absolute Deviation (MAD)4738
Skewness1.6738712
Sum8.2266443 × 108
Variance3.1213522 × 108
MonotonicityNot monotonic
2023-01-05T11:29:45.721767image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8852 129
 
0.3%
8872 124
 
0.3%
8839 123
 
0.3%
8831 122
 
0.3%
8864 122
 
0.3%
8875 121
 
0.3%
8891 120
 
0.3%
8888 120
 
0.3%
8798 118
 
0.3%
8886 117
 
0.3%
Other values (1939) 42281
97.0%
ValueCountFrequency (%)
291 8
< 0.1%
292 6
< 0.1%
293 3
 
< 0.1%
294 5
< 0.1%
295 3
 
< 0.1%
296 6
< 0.1%
297 11
< 0.1%
298 10
< 0.1%
299 5
< 0.1%
300 7
< 0.1%
ValueCountFrequency (%)
73529 21
< 0.1%
73528 30
0.1%
73527 24
0.1%
73526 16
< 0.1%
73525 21
< 0.1%
73524 32
0.1%
73523 25
0.1%
73522 19
< 0.1%
73521 27
0.1%
73520 17
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing88
Missing (%)0.2%
Memory size85.3 KiB
False
28189 
True
15315 
(Missing)
 
88
ValueCountFrequency (%)
False 28189
64.7%
True 15315
35.1%
(Missing) 88
 
0.2%
2023-01-05T11:29:45.821425image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

time period
Real number (ℝ)

Distinct43510
Distinct (%)100.0%
Missing82
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean0.71158876
Minimum0.091397026
Maximum1.4947706
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size340.7 KiB
2023-01-05T11:29:45.901970image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.091397026
5-th percentile0.14253629
Q10.31278715
median0.67351445
Q31.1399499
95-th percentile1.3046842
Maximum1.4947706
Range1.4033736
Interquartile range (IQR)0.82716273

Descriptive statistics

Standard deviation0.41387755
Coefficient of variation (CV)0.58162463
Kurtosis-1.4948724
Mean0.71158876
Median Absolute Deviation (MAD)0.43067733
Skewness0.055738091
Sum30961.227
Variance0.17129462
MonotonicityNot monotonic
2023-01-05T11:29:46.001059image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.3045543636 1
 
< 0.1%
0.8111101801 1
 
< 0.1%
0.8728937482 1
 
< 0.1%
0.3263657326 1
 
< 0.1%
1.117899242 1
 
< 0.1%
0.7345302583 1
 
< 0.1%
0.638880788 1
 
< 0.1%
0.5570088072 1
 
< 0.1%
0.3743610205 1
 
< 0.1%
1.337979481 1
 
< 0.1%
Other values (43500) 43500
99.8%
(Missing) 82
 
0.2%
ValueCountFrequency (%)
0.09139702635 1
< 0.1%
0.09203480245 1
< 0.1%
0.09236694859 1
< 0.1%
0.09242600974 1
< 0.1%
0.09375987052 1
< 0.1%
0.09376263151 1
< 0.1%
0.09378085178 1
< 0.1%
0.09435490014 1
< 0.1%
0.0948472687 1
< 0.1%
0.09488079836 1
< 0.1%
ValueCountFrequency (%)
1.4947706 1
< 0.1%
1.482454102 1
< 0.1%
1.476261286 1
< 0.1%
1.464846911 1
< 0.1%
1.460011403 1
< 0.1%
1.449253529 1
< 0.1%
1.447926053 1
< 0.1%
1.444741504 1
< 0.1%
1.441395727 1
< 0.1%
1.425986831 1
< 0.1%

engine type
Categorical

Distinct11
Distinct (%)< 0.1%
Missing97
Missing (%)0.2%
Memory size340.7 KiB
F8D Petrol Engine
10967 
1.5 L U2 CRDi
10464 
K Series Dual jet
10253 
K10C
3086 
1.2 L K Series Engine
2217 
Other values (6)
6508 

Length

Max length25
Median length21
Mean length14.52629
Min length4

Characters and Unicode

Total characters631821
Distinct characters40
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowK10C
2nd rowK10C
3rd rowK Series Dual jet
4th rowF8D Petrol Engine
5th row1.5 L U2 CRDi

Common Values

ValueCountFrequency (%)
F8D Petrol Engine 10967
25.2%
1.5 L U2 CRDi 10464
24.0%
K Series Dual jet 10253
23.5%
K10C 3086
 
7.1%
1.2 L K Series Engine 2217
 
5.1%
1.0 SCe 1793
 
4.1%
i-DTEC 1575
 
3.6%
1.5 Turbocharged Revotorq 1189
 
2.7%
G12B 897
 
2.1%
1.2 L K12N Dualjet 788
 
1.8%

Length

2023-01-05T11:29:46.488991image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
l 13469
9.4%
engine 13184
9.2%
k 12470
8.7%
series 12470
8.7%
1.5 11919
8.3%
f8d 10967
7.6%
petrol 10967
7.6%
u2 10464
 
7.3%
crdi 10464
 
7.3%
dual 10253
 
7.1%
Other values (12) 26888
18.7%

Most occurring characters

ValueCountFrequency (%)
100020
 
15.8%
e 64835
 
10.3%
i 37693
 
6.0%
D 34047
 
5.4%
r 27802
 
4.4%
n 26634
 
4.2%
t 23463
 
3.7%
l 22008
 
3.5%
1 21488
 
3.4%
C 16918
 
2.7%
Other values (30) 256913
40.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 289373
45.8%
Uppercase Letter 159729
25.3%
Space Separator 100020
 
15.8%
Decimal Number 64407
 
10.2%
Other Punctuation 16717
 
2.6%
Dash Punctuation 1575
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 64835
22.4%
i 37693
13.0%
r 27802
9.6%
n 26634
9.2%
t 23463
 
8.1%
l 22008
 
7.6%
o 15332
 
5.3%
g 14639
 
5.1%
a 12496
 
4.3%
u 12496
 
4.3%
Other values (8) 31975
11.0%
Uppercase Letter
ValueCountFrequency (%)
D 34047
21.3%
C 16918
10.6%
K 16344
10.2%
E 14759
9.2%
S 14263
8.9%
L 13469
 
8.4%
R 11919
 
7.5%
F 10967
 
6.9%
P 10967
 
6.9%
U 10464
 
6.6%
Other values (4) 5612
 
3.5%
Decimal Number
ValueCountFrequency (%)
1 21488
33.4%
2 15154
23.5%
5 11919
18.5%
8 10967
17.0%
0 4879
 
7.6%
Space Separator
ValueCountFrequency (%)
100020
100.0%
Other Punctuation
ValueCountFrequency (%)
. 16717
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1575
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 449102
71.1%
Common 182719
28.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 64835
 
14.4%
i 37693
 
8.4%
D 34047
 
7.6%
r 27802
 
6.2%
n 26634
 
5.9%
t 23463
 
5.2%
l 22008
 
4.9%
C 16918
 
3.8%
K 16344
 
3.6%
o 15332
 
3.4%
Other values (22) 164026
36.5%
Common
ValueCountFrequency (%)
100020
54.7%
1 21488
 
11.8%
. 16717
 
9.1%
2 15154
 
8.3%
5 11919
 
6.5%
8 10967
 
6.0%
0 4879
 
2.7%
- 1575
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 631821
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
100020
 
15.8%
e 64835
 
10.3%
i 37693
 
6.0%
D 34047
 
5.4%
r 27802
 
4.4%
n 26634
 
4.2%
t 23463
 
3.7%
l 22008
 
3.5%
1 21488
 
3.4%
C 16918
 
2.7%
Other values (30) 256913
40.7%

is speed alert
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing61
Missing (%)0.1%
Memory size85.3 KiB
True
43262 
False
 
269
(Missing)
 
61
ValueCountFrequency (%)
True 43262
99.2%
False 269
 
0.6%
(Missing) 61
 
0.1%
2023-01-05T11:29:46.560812image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

steering type
Categorical

Distinct3
Distinct (%)< 0.1%
Missing104
Missing (%)0.2%
Memory size340.7 KiB
Power
24782 
Electric
17808 
Manual
 
898

Length

Max length8
Median length5
Mean length6.2491262
Min length5

Characters and Unicode

Total characters271762
Distinct characters14
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPower
2nd rowPower
3rd rowElectric
4th rowPower
5th rowPower

Common Values

ValueCountFrequency (%)
Power 24782
56.8%
Electric 17808
40.9%
Manual 898
 
2.1%
(Missing) 104
 
0.2%

Length

2023-01-05T11:29:46.624227image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-05T11:29:46.701781image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
power 24782
57.0%
electric 17808
40.9%
manual 898
 
2.1%

Most occurring characters

ValueCountFrequency (%)
e 42590
15.7%
r 42590
15.7%
c 35616
13.1%
P 24782
9.1%
o 24782
9.1%
w 24782
9.1%
l 18706
6.9%
E 17808
6.6%
t 17808
6.6%
i 17808
6.6%
Other values (4) 4490
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 228274
84.0%
Uppercase Letter 43488
 
16.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 42590
18.7%
r 42590
18.7%
c 35616
15.6%
o 24782
10.9%
w 24782
10.9%
l 18706
8.2%
t 17808
7.8%
i 17808
7.8%
a 1796
 
0.8%
n 898
 
0.4%
Uppercase Letter
ValueCountFrequency (%)
P 24782
57.0%
E 17808
40.9%
M 898
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 271762
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 42590
15.7%
r 42590
15.7%
c 35616
13.1%
P 24782
9.1%
o 24782
9.1%
w 24782
9.1%
l 18706
6.9%
E 17808
6.6%
t 17808
6.6%
i 17808
6.6%
Other values (4) 4490
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 271762
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 42590
15.7%
r 42590
15.7%
c 35616
13.1%
P 24782
9.1%
o 24782
9.1%
w 24782
9.1%
l 18706
6.9%
E 17808
6.6%
t 17808
6.6%
i 17808
6.6%
Other values (4) 4490
 
1.7%

length
Real number (ℝ)

Distinct9
Distinct (%)< 0.1%
Missing88
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean3852.2709
Minimum3445
Maximum4300
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size340.7 KiB
2023-01-05T11:29:46.764491image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum3445
5-th percentile3445
Q13445
median3845
Q33995
95-th percentile4300
Maximum4300
Range855
Interquartile range (IQR)550

Descriptive statistics

Standard deviation311.12424
Coefficient of variation (CV)0.080763851
Kurtosis-1.2074774
Mean3852.2709
Median Absolute Deviation (MAD)190
Skewness0.13931821
Sum1.675892 × 108
Variance96798.29
MonotonicityNot monotonic
2023-01-05T11:29:46.823261image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
3445 10973
25.2%
4300 10464
24.0%
3845 10251
23.5%
3990 3405
 
7.8%
3655 3087
 
7.1%
3995 2364
 
5.4%
3731 1796
 
4.1%
3675 896
 
2.1%
3993 268
 
0.6%
(Missing) 88
 
0.2%
ValueCountFrequency (%)
3445 10973
25.2%
3655 3087
 
7.1%
3675 896
 
2.1%
3731 1796
 
4.1%
3845 10251
23.5%
3990 3405
 
7.8%
3993 268
 
0.6%
3995 2364
 
5.4%
4300 10464
24.0%
ValueCountFrequency (%)
4300 10464
24.0%
3995 2364
 
5.4%
3993 268
 
0.6%
3990 3405
 
7.8%
3845 10251
23.5%
3731 1796
 
4.1%
3675 896
 
2.1%
3655 3087
 
7.1%
3445 10973
25.2%

width
Real number (ℝ)

Distinct10
Distinct (%)< 0.1%
Missing74
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean1672.8779
Minimum1475
Maximum1811
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size340.7 KiB
2023-01-05T11:29:46.887876image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1475
5-th percentile1515
Q11515
median1735
Q31755
95-th percentile1790
Maximum1811
Range336
Interquartile range (IQR)240

Descriptive statistics

Standard deviation111.89173
Coefficient of variation (CV)0.066885773
Kurtosis-1.4306815
Mean1672.8779
Median Absolute Deviation (MAD)55
Skewness-0.49864897
Sum72800302
Variance12519.76
MonotonicityNot monotonic
2023-01-05T11:29:46.952763image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1735 11049
25.3%
1515 10975
25.2%
1790 10470
24.0%
1620 3082
 
7.1%
1745 2214
 
5.1%
1579 1797
 
4.1%
1695 1578
 
3.6%
1755 1187
 
2.7%
1475 897
 
2.1%
1811 269
 
0.6%
(Missing) 74
 
0.2%
ValueCountFrequency (%)
1475 897
 
2.1%
1515 10975
25.2%
1579 1797
 
4.1%
1620 3082
 
7.1%
1695 1578
 
3.6%
1735 11049
25.3%
1745 2214
 
5.1%
1755 1187
 
2.7%
1790 10470
24.0%
1811 269
 
0.6%
ValueCountFrequency (%)
1811 269
 
0.6%
1790 10470
24.0%
1755 1187
 
2.7%
1745 2214
 
5.1%
1735 11049
25.3%
1695 1578
 
3.6%
1620 3082
 
7.1%
1579 1797
 
4.1%
1515 10975
25.2%
1475 897
 
2.1%

is parking sensors
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing81
Missing (%)0.2%
Memory size85.3 KiB
True
41715 
False
 
1796
(Missing)
 
81
ValueCountFrequency (%)
True 41715
95.7%
False 1796
 
4.1%
(Missing) 81
 
0.2%
2023-01-05T11:29:47.033606image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

power
Categorical

Distinct9
Distinct (%)< 0.1%
Missing97
Missing (%)0.2%
Memory size340.7 KiB
88.50bhp@6000rpm
13252 
40.36bhp@6000rpm
10966 
113.45bhp@4000rpm
10468 
55.92bhp@5300rpm
3088 
67.06bhp@5500rpm
1793 
Other values (4)
3928 

Length

Max length17
Median length16
Mean length16.246833
Min length16

Characters and Unicode

Total characters706656
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row55.92bhp@5300rpm
2nd row55.92bhp@5300rpm
3rd row88.50bhp@6000rpm
4th row40.36bhp@6000rpm
5th row113.45bhp@4000rpm

Common Values

ValueCountFrequency (%)
88.50bhp@6000rpm 13252
30.4%
40.36bhp@6000rpm 10966
25.2%
113.45bhp@4000rpm 10468
24.0%
55.92bhp@5300rpm 3088
 
7.1%
67.06bhp@5500rpm 1793
 
4.1%
97.89bhp@3600rpm 1575
 
3.6%
88.77bhp@4000rpm 1187
 
2.7%
61.68bhp@6000rpm 898
 
2.1%
118.36bhp@5500rpm 268
 
0.6%
(Missing) 97
 
0.2%

Length

2023-01-05T11:29:47.098993image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-05T11:29:47.191892image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
88.50bhp@6000rpm 13252
30.5%
40.36bhp@6000rpm 10966
25.2%
113.45bhp@4000rpm 10468
24.1%
55.92bhp@5300rpm 3088
 
7.1%
67.06bhp@5500rpm 1793
 
4.1%
97.89bhp@3600rpm 1575
 
3.6%
88.77bhp@4000rpm 1187
 
2.7%
61.68bhp@6000rpm 898
 
2.1%
118.36bhp@5500rpm 268
 
0.6%

Most occurring characters

ValueCountFrequency (%)
0 149772
21.2%
p 86990
12.3%
b 43495
 
6.2%
h 43495
 
6.2%
@ 43495
 
6.2%
. 43495
 
6.2%
r 43495
 
6.2%
m 43495
 
6.2%
6 43307
 
6.1%
5 37106
 
5.3%
Other values (7) 128511
18.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 358696
50.8%
Lowercase Letter 260970
36.9%
Other Punctuation 86990
 
12.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 149772
41.8%
6 43307
 
12.1%
5 37106
 
10.3%
4 33089
 
9.2%
8 31619
 
8.8%
3 26365
 
7.4%
1 22370
 
6.2%
9 6238
 
1.7%
7 5742
 
1.6%
2 3088
 
0.9%
Lowercase Letter
ValueCountFrequency (%)
p 86990
33.3%
b 43495
16.7%
h 43495
16.7%
r 43495
16.7%
m 43495
16.7%
Other Punctuation
ValueCountFrequency (%)
@ 43495
50.0%
. 43495
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 445686
63.1%
Latin 260970
36.9%

Most frequent character per script

Common
ValueCountFrequency (%)
0 149772
33.6%
@ 43495
 
9.8%
. 43495
 
9.8%
6 43307
 
9.7%
5 37106
 
8.3%
4 33089
 
7.4%
8 31619
 
7.1%
3 26365
 
5.9%
1 22370
 
5.0%
9 6238
 
1.4%
Other values (2) 8830
 
2.0%
Latin
ValueCountFrequency (%)
p 86990
33.3%
b 43495
16.7%
h 43495
16.7%
r 43495
16.7%
m 43495
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 706656
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 149772
21.2%
p 86990
12.3%
b 43495
 
6.2%
h 43495
 
6.2%
@ 43495
 
6.2%
. 43495
 
6.2%
r 43495
 
6.2%
m 43495
 
6.2%
6 43307
 
6.1%
5 37106
 
5.3%
Other values (7) 128511
18.2%

gross weight
Real number (ℝ)

Distinct10
Distinct (%)< 0.1%
Missing97
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean1386.2213
Minimum1051
Maximum1720
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size340.7 KiB
2023-01-05T11:29:47.277250image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1051
5-th percentile1155
Q11185
median1335
Q31510
95-th percentile1720
Maximum1720
Range669
Interquartile range (IQR)325

Descriptive statistics

Standard deviation212.6551
Coefficient of variation (CV)0.15340632
Kurtosis-1.0335397
Mean1386.2213
Median Absolute Deviation (MAD)150
Skewness0.53893433
Sum60293694
Variance45222.193
MonotonicityNot monotonic
2023-01-05T11:29:47.340719image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1335 11037
25.3%
1185 10962
25.1%
1720 10472
24.0%
1340 3083
 
7.1%
1410 2215
 
5.1%
1155 1795
 
4.1%
1051 1574
 
3.6%
1490 1190
 
2.7%
1510 898
 
2.1%
1660 269
 
0.6%
(Missing) 97
 
0.2%
ValueCountFrequency (%)
1051 1574
 
3.6%
1155 1795
 
4.1%
1185 10962
25.1%
1335 11037
25.3%
1340 3083
 
7.1%
1410 2215
 
5.1%
1490 1190
 
2.7%
1510 898
 
2.1%
1660 269
 
0.6%
1720 10472
24.0%
ValueCountFrequency (%)
1720 10472
24.0%
1660 269
 
0.6%
1510 898
 
2.1%
1490 1190
 
2.7%
1410 2215
 
5.1%
1340 3083
 
7.1%
1335 11037
25.3%
1185 10962
25.1%
1155 1795
 
4.1%
1051 1574
 
3.6%
Distinct2
Distinct (%)< 0.1%
Missing96
Missing (%)0.2%
Memory size85.3 KiB
False
26873 
True
16623 
(Missing)
 
96
ValueCountFrequency (%)
False 26873
61.6%
True 16623
38.1%
(Missing) 96
 
0.2%
2023-01-05T11:29:47.422684image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

model
Categorical

Distinct11
Distinct (%)< 0.1%
Missing86
Missing (%)0.2%
Memory size340.7 KiB
Model_1
10965 
Model_4
10469 
Model_6
10257 
Model_8
3084 
Model_7
2218 
Other values (6)
6513 

Length

Max length8
Median length7
Mean length7.0268239
Min length7

Characters and Unicode

Total characters305709
Distinct characters16
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowModel_8
2nd rowModel_8
3rd rowModel_6
4th rowModel_1
5th rowModel_4

Common Values

ValueCountFrequency (%)
Model_1 10965
25.2%
Model_4 10469
24.0%
Model_6 10257
23.5%
Model_8 3084
 
7.1%
Model_7 2218
 
5.1%
Model_3 1793
 
4.1%
Model_9 1576
 
3.6%
Model_5 1190
 
2.7%
Model_10 898
 
2.1%
Model_2 787
 
1.8%

Length

2023-01-05T11:29:47.488496image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
model_1 10965
25.2%
model_4 10469
24.1%
model_6 10257
23.6%
model_8 3084
 
7.1%
model_7 2218
 
5.1%
model_3 1793
 
4.1%
model_9 1576
 
3.6%
model_5 1190
 
2.7%
model_10 898
 
2.1%
model_2 787
 
1.8%

Most occurring characters

ValueCountFrequency (%)
M 43506
14.2%
o 43506
14.2%
d 43506
14.2%
e 43506
14.2%
l 43506
14.2%
_ 43506
14.2%
1 12401
 
4.1%
4 10469
 
3.4%
6 10257
 
3.4%
8 3084
 
1.0%
Other values (6) 8462
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 174024
56.9%
Decimal Number 44673
 
14.6%
Uppercase Letter 43506
 
14.2%
Connector Punctuation 43506
 
14.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 12401
27.8%
4 10469
23.4%
6 10257
23.0%
8 3084
 
6.9%
7 2218
 
5.0%
3 1793
 
4.0%
9 1576
 
3.5%
5 1190
 
2.7%
0 898
 
2.0%
2 787
 
1.8%
Lowercase Letter
ValueCountFrequency (%)
o 43506
25.0%
d 43506
25.0%
e 43506
25.0%
l 43506
25.0%
Uppercase Letter
ValueCountFrequency (%)
M 43506
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 43506
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 217530
71.2%
Common 88179
28.8%

Most frequent character per script

Common
ValueCountFrequency (%)
_ 43506
49.3%
1 12401
 
14.1%
4 10469
 
11.9%
6 10257
 
11.6%
8 3084
 
3.5%
7 2218
 
2.5%
3 1793
 
2.0%
9 1576
 
1.8%
5 1190
 
1.3%
0 898
 
1.0%
Latin
ValueCountFrequency (%)
M 43506
20.0%
o 43506
20.0%
d 43506
20.0%
e 43506
20.0%
l 43506
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 305709
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
M 43506
14.2%
o 43506
14.2%
d 43506
14.2%
e 43506
14.2%
l 43506
14.2%
_ 43506
14.2%
1 12401
 
4.1%
4 10469
 
3.4%
6 10257
 
3.4%
8 3084
 
1.0%
Other values (6) 8462
 
2.8%

gear box
Categorical

Distinct2
Distinct (%)< 0.1%
Missing116
Missing (%)0.3%
Memory size340.7 KiB
5.0
32746 
6.0
10730 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters130428
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5.0
2nd row5.0
3rd row5.0
4th row5.0
5th row6.0

Common Values

ValueCountFrequency (%)
5.0 32746
75.1%
6.0 10730
 
24.6%
(Missing) 116
 
0.3%

Length

2023-01-05T11:29:47.558346image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-05T11:29:47.631834image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
5.0 32746
75.3%
6.0 10730
 
24.7%

Most occurring characters

ValueCountFrequency (%)
. 43476
33.3%
0 43476
33.3%
5 32746
25.1%
6 10730
 
8.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 86952
66.7%
Other Punctuation 43476
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 43476
50.0%
5 32746
37.7%
6 10730
 
12.3%
Other Punctuation
ValueCountFrequency (%)
. 43476
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 130428
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 43476
33.3%
0 43476
33.3%
5 32746
25.1%
6 10730
 
8.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 130428
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 43476
33.3%
0 43476
33.3%
5 32746
25.1%
6 10730
 
8.2%

airbags
Categorical

Distinct3
Distinct (%)< 0.1%
Missing86
Missing (%)0.2%
Memory size340.7 KiB
2.0
29923 
6.0
12684 
1.0
 
899

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters130518
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row2.0
4th row2.0
5th row6.0

Common Values

ValueCountFrequency (%)
2.0 29923
68.6%
6.0 12684
29.1%
1.0 899
 
2.1%
(Missing) 86
 
0.2%

Length

2023-01-05T11:29:47.695566image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-05T11:29:47.772091image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
2.0 29923
68.8%
6.0 12684
29.2%
1.0 899
 
2.1%

Most occurring characters

ValueCountFrequency (%)
. 43506
33.3%
0 43506
33.3%
2 29923
22.9%
6 12684
 
9.7%
1 899
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 87012
66.7%
Other Punctuation 43506
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 43506
50.0%
2 29923
34.4%
6 12684
 
14.6%
1 899
 
1.0%
Other Punctuation
ValueCountFrequency (%)
. 43506
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 130518
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 43506
33.3%
0 43506
33.3%
2 29923
22.9%
6 12684
 
9.7%
1 899
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 130518
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 43506
33.3%
0 43506
33.3%
2 29923
22.9%
6 12684
 
9.7%
1 899
 
0.7%

turning radius
Real number (ℝ)

Distinct9
Distinct (%)< 0.1%
Missing98
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean4.8542627
Minimum4.5
Maximum5.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size340.7 KiB
2023-01-05T11:29:47.837984image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum4.5
5-th percentile4.6
Q14.6
median4.8
Q35
95-th percentile5.2
Maximum5.2
Range0.7
Interquartile range (IQR)0.4

Descriptive statistics

Standard deviation0.22801687
Coefficient of variation (CV)0.046972504
Kurtosis-1.1826165
Mean4.8542627
Median Absolute Deviation (MAD)0.2
Skewness0.41225208
Sum211131.3
Variance0.051991693
MonotonicityNot monotonic
2023-01-05T11:29:47.903722image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
4.8 11037
25.3%
4.6 10969
25.2%
5.2 10464
24.0%
4.7 3082
 
7.1%
5 2985
 
6.8%
4.85 2220
 
5.1%
4.9 1572
 
3.6%
4.5 897
 
2.1%
5.1 268
 
0.6%
(Missing) 98
 
0.2%
ValueCountFrequency (%)
4.5 897
 
2.1%
4.6 10969
25.2%
4.7 3082
 
7.1%
4.8 11037
25.3%
4.85 2220
 
5.1%
4.9 1572
 
3.6%
5 2985
 
6.8%
5.1 268
 
0.6%
5.2 10464
24.0%
ValueCountFrequency (%)
5.2 10464
24.0%
5.1 268
 
0.6%
5 2985
 
6.8%
4.9 1572
 
3.6%
4.85 2220
 
5.1%
4.8 11037
25.3%
4.7 3082
 
7.1%
4.6 10969
25.2%
4.5 897
 
2.1%
Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size340.7 KiB
1
8858 
2
8778 
4
8736 
5
8644 
3
8576 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters43592
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row1
3rd row5
4th row2
5th row3

Common Values

ValueCountFrequency (%)
1 8858
20.3%
2 8778
20.1%
4 8736
20.0%
5 8644
19.8%
3 8576
19.7%

Length

2023-01-05T11:29:47.978963image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-05T11:29:48.060943image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1 8858
20.3%
2 8778
20.1%
4 8736
20.0%
5 8644
19.8%
3 8576
19.7%

Most occurring characters

ValueCountFrequency (%)
1 8858
20.3%
2 8778
20.1%
4 8736
20.0%
5 8644
19.8%
3 8576
19.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 43592
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 8858
20.3%
2 8778
20.1%
4 8736
20.0%
5 8644
19.8%
3 8576
19.7%

Most occurring scripts

ValueCountFrequency (%)
Common 43592
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 8858
20.3%
2 8778
20.1%
4 8736
20.0%
5 8644
19.8%
3 8576
19.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 43592
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 8858
20.3%
2 8778
20.1%
4 8736
20.0%
5 8644
19.8%
3 8576
19.7%

is claim
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size340.7 KiB
0
40804 
1
 
2788

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters43592
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 40804
93.6%
1 2788
 
6.4%

Length

2023-01-05T11:29:48.135954image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-05T11:29:48.207668image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 40804
93.6%
1 2788
 
6.4%

Most occurring characters

ValueCountFrequency (%)
0 40804
93.6%
1 2788
 
6.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 43592
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 40804
93.6%
1 2788
 
6.4%

Most occurring scripts

ValueCountFrequency (%)
Common 43592
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 40804
93.6%
1 2788
 
6.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 43592
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 40804
93.6%
1 2788
 
6.4%

Interactions

2023-01-05T11:29:38.570474image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-05T11:29:28.064574image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-05T11:29:29.162467image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-05T11:29:30.187921image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-05T11:29:31.153087image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-05T11:29:32.173124image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-05T11:29:33.227991image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-05T11:29:34.469614image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-05T11:29:35.521219image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-05T11:29:36.489950image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-05T11:29:37.533674image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-05T11:29:38.661379image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-05T11:29:28.145313image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-05T11:29:29.254332image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-05T11:29:30.268048image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-05T11:29:31.238648image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-05T11:29:32.264706image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-05T11:29:33.317540image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-05T11:29:34.555902image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-05T11:29:35.603414image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-05T11:29:36.576738image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-05T11:29:37.624784image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-05T11:29:38.763941image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-05T11:29:28.232417image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-05T11:29:29.348695image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-05T11:29:30.368664image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-05T11:29:31.333657image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-05T11:29:32.365122image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-05T11:29:33.414377image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-05T11:29:34.651871image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-05T11:29:35.697453image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-05T11:29:36.672796image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-05T11:29:37.724441image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-05T11:29:38.859322image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-05T11:29:28.313435image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-05T11:29:29.438793image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-05T11:29:30.455807image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-05T11:29:31.421714image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-05T11:29:32.459567image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-05T11:29:33.506479image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-05T11:29:34.742753image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-05T11:29:35.783653image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-05T11:29:36.766415image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-05T11:29:37.816561image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-05T11:29:38.958531image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-05T11:29:28.396492image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-05T11:29:29.530071image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-05T11:29:30.540745image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-05T11:29:31.512473image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-05T11:29:32.557613image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-05T11:29:33.815303image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-05T11:29:34.837878image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-05T11:29:35.875085image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-05T11:29:36.862060image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-05T11:29:37.910562image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-05T11:29:39.057171image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-05T11:29:28.482742image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-05T11:29:29.622658image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-05T11:29:30.632391image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-05T11:29:31.606447image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-05T11:29:32.655837image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-05T11:29:33.904036image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-05T11:29:34.935212image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-05T11:29:35.966576image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-05T11:29:36.964386image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-05T11:29:38.005933image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-05T11:29:39.155416image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-05T11:29:28.567240image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-05T11:29:29.714759image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-05T11:29:30.720531image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-05T11:29:31.700212image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-05T11:29:32.755657image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-05T11:29:33.993239image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-05T11:29:35.032895image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-05T11:29:36.056339image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-05T11:29:37.062310image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-05T11:29:38.102764image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-05T11:29:39.252260image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-05T11:29:28.647102image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-05T11:29:29.810546image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-05T11:29:30.805225image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-05T11:29:31.791482image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-05T11:29:32.850532image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-05T11:29:34.088696image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-05T11:29:35.124822image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-05T11:29:36.145018image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-05T11:29:37.156338image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-05T11:29:38.197469image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-05T11:29:39.341337image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-05T11:29:28.908579image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-05T11:29:29.892927image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-05T11:29:30.884657image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-05T11:29:31.876538image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-05T11:29:32.938384image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-05T11:29:34.176435image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-05T11:29:35.211331image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-05T11:29:36.223626image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-05T11:29:37.245067image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-05T11:29:38.285019image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-05T11:29:39.441932image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-05T11:29:28.990265image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-05T11:29:30.006445image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-05T11:29:30.969925image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-05T11:29:31.969984image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-05T11:29:33.034126image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-05T11:29:34.273566image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-05T11:29:35.321239image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-05T11:29:36.311965image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-05T11:29:37.338618image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-05T11:29:38.380797image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-05T11:29:39.539999image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-05T11:29:29.074388image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-05T11:29:30.094920image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-05T11:29:31.061717image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-05T11:29:32.072112image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-05T11:29:33.130364image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-05T11:29:34.370929image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-05T11:29:35.425289image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-05T11:29:36.399397image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-05T11:29:37.435755image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-05T11:29:38.473534image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2023-01-05T11:29:48.308814image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
IDpolicyholder agecar ageheightengine volumepopulationtime periodlengthwidthgross weightturning radiusncap ratingis power door locksis parking camerarear brakes typeis adjustable steeringis tpmsis driver seat height adjustablesegmentis central lockingis rear window wipercluster areais ecwfuel typetorquetransmission typemanufacturercylinderis rear window washeris front fog lightsis brake assistis power steeringis escis rear window defoggerengine typeis speed alertsteering typeis parking sensorspoweris day night rear view mirrormodelgear boxairbagsarea danger levelis claim
ID1.0000.006-0.006-0.001-0.0050.0050.000-0.003-0.004-0.003-0.0040.0000.0060.0090.0120.0000.0130.0000.0080.0060.0140.0000.0070.0000.0000.0090.0000.0000.0150.0000.0000.0000.0090.0090.0050.0000.0000.0000.0000.0000.0050.0120.0090.0070.000
policyholder age0.0061.000-0.048-0.055-0.0280.0290.135-0.028-0.013-0.022-0.0270.0370.0660.0480.0180.0410.0180.0510.0450.0660.0230.0380.0660.0360.0410.0370.0310.0390.0230.0510.0520.0220.0270.0370.0410.0080.0260.0300.0410.0410.0410.0200.0230.0000.026
car age-0.006-0.0481.0000.3840.413-0.0000.1960.4200.4130.3400.3970.1420.3040.1510.1480.2700.1480.2630.1390.3040.1560.0730.3040.2030.1260.1500.0860.2760.1560.2550.2630.0170.1620.1540.1260.0460.1010.0000.1260.1320.1260.1550.1120.0000.017
height-0.001-0.0550.3841.0000.599-0.0090.1740.6430.6340.8140.5540.7070.7560.7561.0000.8471.0000.7880.8670.7560.8870.1490.7560.8050.9730.7860.5770.8440.8870.7890.8501.0000.8470.7941.0001.0000.9060.2650.9730.7311.0001.0000.9450.0030.009
engine volume-0.005-0.0280.4130.5991.000-0.0070.1900.9540.8780.6560.8970.7420.9500.7360.8510.9470.8510.9040.8740.9500.7400.2340.9500.9321.0000.6520.6600.9870.7400.8920.8480.2060.7060.7971.0000.1120.5790.5841.0000.8061.0000.8350.5420.0000.006
population0.0050.029-0.000-0.009-0.0071.000-0.034-0.012-0.015-0.020-0.0070.1450.3100.1500.1480.2700.1480.2630.1400.3100.1511.0000.3100.1980.1360.1490.0810.2770.1510.2630.2580.0200.1510.1500.1380.0000.1050.0360.1360.1390.1380.1470.1090.0000.031
time period0.0000.1350.1960.1740.190-0.0341.0000.1940.1950.1510.1870.1260.2820.1440.1200.2370.1200.2360.1200.2820.1320.0980.2820.1810.1030.1380.0660.2360.1320.2340.2260.0220.1340.1380.0980.0110.1000.0210.1030.1400.0970.1210.0940.0000.082
length-0.003-0.0280.4200.6430.954-0.0120.1941.0000.9460.7670.9410.7530.9590.9531.0000.9881.0000.9540.8160.9590.9190.1860.9590.9460.9370.9210.7820.9520.9190.9460.9280.4560.9170.9511.0000.1960.7661.0000.9370.9451.0000.9840.7230.0000.006
width-0.004-0.0130.4130.6340.878-0.0150.1950.9461.0000.8290.9220.8961.0000.9250.9830.9870.9830.9630.9531.0000.9410.1571.0000.9630.9480.9060.9000.9870.9410.9500.9631.0000.9160.9211.0000.1371.0001.0000.9480.9621.0001.0000.9710.0010.005
gross weight-0.003-0.0220.3400.8140.656-0.0200.1510.7670.8291.0000.6710.9230.9700.9630.9830.8320.9830.8780.7740.9700.9850.1680.9700.8860.9160.9480.9250.8580.9850.8650.8810.6460.9600.9621.0000.1370.7651.0000.9160.8751.0001.0000.8320.0000.000
turning radius-0.004-0.0270.3970.5540.897-0.0070.1870.9410.9220.6711.0000.8971.0000.8411.0000.9651.0001.0000.9541.0000.8910.1561.0000.9741.0000.8330.8820.9640.8911.0001.0001.0000.8670.8751.0001.0001.0000.7651.0000.9641.0001.0000.9470.0000.007
ncap rating0.0000.0370.1420.7070.7420.1450.1260.7530.8960.9230.8971.0000.8850.7681.0000.6871.0000.6930.7340.8850.8900.1770.8850.8350.9690.7700.8740.6370.8900.7100.7010.2090.8360.8451.0000.4240.5480.2730.9690.7591.0000.9860.6460.0000.000
is power door locks0.0060.0660.3040.7560.9500.3100.2820.9591.0000.9701.0000.8851.0000.4930.3440.7640.3440.7310.9061.0000.3930.3821.0000.8461.0000.4500.4510.7020.3930.7220.6790.2360.4160.4511.0000.0480.5400.1271.0000.4811.0000.3500.4410.0000.003
is parking camera0.0090.0480.1510.7560.7360.1500.1440.9530.9250.9630.8410.7680.4931.0000.6990.4460.6990.5030.8200.4930.7970.1840.4930.7540.8810.9130.8490.4160.7970.4860.4070.1170.8440.9151.0000.0970.1390.2570.8810.0151.0000.7110.7980.0000.000
rear brakes type0.0120.0180.1481.0000.8510.1480.1201.0000.9830.9831.0001.0000.3440.6991.0000.4511.0000.4711.0000.3440.8770.1710.3450.8511.0000.7651.0000.4320.8770.4770.5080.0810.8280.7631.0000.0440.4890.1171.0000.4431.0000.9830.8770.0000.000
is adjustable steering0.0000.0410.2700.8470.9470.2700.2370.9880.9870.8320.9650.6870.7640.4460.4511.0000.4510.9310.9880.7640.5140.3300.7640.9101.0000.4120.5480.9570.5140.9450.8630.1810.5170.5641.0000.0980.5110.2591.0000.4561.0000.4290.5310.0000.010
is tpms0.0130.0180.1481.0000.8510.1480.1201.0000.9830.9831.0001.0000.3440.6991.0000.4511.0000.4711.0000.3440.8770.1710.3450.8511.0000.7661.0000.4320.8770.4770.5080.0810.8280.7631.0000.0440.4890.1171.0000.4431.0000.9830.8770.0000.000
is driver seat height adjustable0.0000.0510.2630.7880.9040.2630.2360.9540.9630.8781.0000.6930.7310.5030.4710.9310.4711.0000.9470.7310.5370.3200.7310.8641.0000.4400.5590.8900.5370.9870.9290.1730.5690.6181.0000.0660.4330.2481.0000.4861.0000.4790.5520.0000.007
segment0.0080.0450.1390.8670.8740.1400.1200.8160.9530.7740.9540.7340.9060.8201.0000.9881.0000.9471.0000.9060.8910.1720.9060.8810.9660.7750.6550.9880.8910.9350.9171.0000.8570.9011.0000.3100.9560.3220.9660.8491.0000.9850.9460.0000.005
is central locking0.0060.0660.3040.7560.9500.3100.2820.9591.0000.9701.0000.8851.0000.4930.3440.7640.3440.7310.9061.0000.3930.3831.0000.8461.0000.4500.4510.7030.3930.7220.6790.2360.4160.4511.0000.0480.5400.1271.0000.4821.0000.3500.4410.0000.003
is rear window wiper0.0140.0230.1560.8870.7400.1510.1320.9190.9410.9850.8910.8900.3930.7970.8770.5140.8770.5370.8910.3931.0000.1770.3930.7380.8910.8720.8790.4921.0000.5440.5790.0930.9440.8701.0000.0500.3330.1330.8910.2741.0000.8611.0000.0000.000
cluster area0.0000.0380.0730.1490.2341.0000.0980.1860.1570.1680.1560.1770.3820.1840.1710.3300.1710.3200.1720.3830.1771.0000.3830.2470.1460.1800.0970.3390.1770.3190.3100.0260.1770.1800.1320.0000.1420.0440.1460.1880.1320.1700.1280.0060.035
is ecw0.0070.0660.3040.7560.9500.3100.2820.9591.0000.9701.0000.8851.0000.4930.3450.7640.3450.7310.9061.0000.3930.3831.0000.8461.0000.4500.4510.7030.3930.7220.6790.2360.4160.4521.0000.0480.5400.1271.0000.4811.0000.3500.4420.0000.004
fuel type0.0000.0360.2030.8050.9320.1980.1810.9460.9630.8860.9740.8350.8460.7540.8510.9100.8510.8640.8810.8460.7380.2470.8461.0001.0000.6680.7210.8610.7390.8540.8060.2000.7020.7951.0000.1070.6280.2811.0000.9161.0000.8350.5360.0000.004
torque0.0000.0410.1260.9731.0000.1360.1030.9370.9480.9161.0000.9691.0000.8811.0001.0001.0001.0000.9661.0000.8910.1461.0001.0001.0000.8751.0001.0000.8911.0001.0001.0000.8680.8751.0001.0001.0001.0001.0001.0001.0001.0000.9470.0000.008
transmission type0.0090.0370.1500.7860.6520.1490.1380.9210.9060.9480.8330.7700.4500.9130.7650.4120.7660.4400.7750.4500.8720.1800.4500.6680.8751.0000.8540.3850.8720.4480.4890.1060.8960.8161.0000.0580.1890.2820.8750.1021.0000.7480.8730.0000.000
manufacturer0.0000.0310.0860.5770.6600.0810.0660.7820.9000.9250.8820.8740.4510.8491.0000.5481.0000.5590.6550.4510.8790.0970.4510.7211.0000.8541.0000.5320.8790.5800.5710.1060.8310.8411.0000.4230.4071.0001.0000.5621.0000.9860.6240.0000.000
cylinder0.0000.0390.2760.8440.9870.2770.2360.9520.9870.8580.9640.6370.7020.4160.4320.9570.4320.8900.9880.7030.4920.3390.7030.8611.0000.3850.5321.0000.4920.9040.8250.1110.4940.5391.0000.1020.4950.2701.0000.4271.0000.4090.5170.0000.008
is rear window washer0.0150.0230.1560.8870.7400.1510.1320.9190.9410.9850.8910.8900.3930.7970.8770.5140.8770.5370.8910.3931.0000.1770.3930.7390.8910.8720.8790.4921.0000.5440.5780.0930.9440.8701.0000.0500.3330.1330.8910.2741.0000.8601.0000.0000.000
is front fog lights0.0000.0510.2550.7890.8920.2630.2340.9460.9500.8651.0000.7100.7220.4860.4770.9450.4770.9870.9350.7220.5440.3190.7220.8541.0000.4480.5800.9040.5441.0000.9160.1710.5500.6001.0000.0920.4410.2441.0000.4951.0000.4570.5580.0000.008
is brake assist0.0000.0520.2630.8500.8480.2580.2260.9280.9630.8811.0000.7010.6790.4070.5080.8630.5080.9290.9170.6790.5790.3100.6790.8061.0000.4890.5710.8250.5780.9161.0000.1610.6130.5121.0000.0710.3460.2301.0000.3881.0000.5160.5890.0000.006
is power steering0.0000.0220.0171.0000.2060.0200.0220.4561.0000.6461.0000.2090.2360.1170.0810.1810.0810.1731.0000.2360.0930.0260.2360.2001.0000.1060.1060.1110.0930.1710.1611.0000.0980.1071.0000.0091.0000.0291.0000.1141.0000.0831.0000.0010.000
is esc0.0090.0270.1620.8470.7060.1510.1340.9170.9160.9600.8670.8360.4160.8440.8280.5170.8280.5690.8570.4160.9440.1770.4160.7020.8680.8960.8310.4940.9440.5500.6130.0981.0000.9221.0000.1160.2940.1410.8680.2281.0000.8420.9440.0000.000
is rear window defogger0.0090.0370.1540.7940.7970.1500.1380.9510.9210.9620.8750.8450.4510.9150.7630.5640.7630.6180.9010.4510.8700.1800.4520.7950.8750.8160.8410.5390.8700.6000.5120.1070.9221.0001.0000.1070.2090.1530.8750.1261.0000.7760.8710.0000.000
engine type0.0050.0410.1261.0001.0000.1380.0981.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.1321.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.0000.006
is speed alert0.0000.0080.0461.0000.1120.0000.0110.1960.1370.1371.0000.4240.0480.0970.0440.0980.0440.0660.3100.0480.0500.0000.0480.1071.0000.0580.4230.1020.0500.0920.0710.0090.1160.1071.0001.0000.0680.0151.0000.0621.0000.1380.0530.0070.006
steering type0.0000.0260.1010.9060.5790.1050.1000.7661.0000.7651.0000.5480.5400.1390.4890.5110.4890.4330.9560.5400.3330.1420.5400.6281.0000.1890.4070.4950.3330.4410.3461.0000.2940.2091.0000.0681.0000.2491.0000.9441.0000.4970.7430.0000.007
is parking sensors0.0000.0300.0000.2650.5840.0360.0211.0001.0001.0000.7650.2730.1270.2570.1170.2590.1170.2480.3220.1270.1330.0440.1270.2811.0000.2821.0000.2700.1330.2440.2300.0290.1410.1531.0000.0150.2491.0001.0000.2641.0000.1190.1400.0000.004
power0.0000.0410.1260.9731.0000.1360.1030.9370.9480.9161.0000.9691.0000.8811.0001.0001.0001.0000.9661.0000.8910.1461.0001.0001.0000.8751.0001.0000.8911.0001.0001.0000.8680.8751.0001.0001.0001.0001.0001.0001.0001.0000.9470.0000.008
is day night rear view mirror0.0000.0410.1320.7310.8060.1390.1400.9450.9620.8750.9640.7590.4810.0150.4430.4560.4430.4860.8490.4820.2740.1880.4810.9161.0000.1020.5620.4270.2740.4950.3880.1140.2280.1261.0000.0620.9440.2641.0001.0001.0000.4500.3080.0000.007
model0.0050.0410.1261.0001.0000.1380.0971.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.1321.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.0000.006
gear box0.0120.0200.1551.0000.8350.1470.1210.9841.0001.0001.0000.9860.3500.7110.9830.4290.9830.4790.9850.3500.8610.1700.3500.8351.0000.7480.9860.4090.8600.4570.5160.0830.8420.7761.0000.1380.4970.1191.0000.4501.0001.0000.8610.0000.000
airbags0.0090.0230.1120.9450.5420.1090.0940.7230.9710.8320.9470.6460.4410.7980.8770.5310.8770.5520.9460.4411.0000.1280.4420.5360.9470.8730.6240.5171.0000.5580.5891.0000.9440.8711.0000.0530.7430.1400.9470.3081.0000.8611.0000.0000.000
area danger level0.0070.0000.0000.0030.0000.0000.0000.0000.0010.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0060.0000.0000.0000.0000.0000.0000.0000.0000.0000.0010.0000.0000.0000.0070.0000.0000.0000.0000.0000.0000.0001.0000.000
is claim0.0000.0260.0170.0090.0060.0310.0820.0060.0050.0000.0070.0000.0030.0000.0000.0100.0000.0070.0050.0030.0000.0350.0040.0040.0080.0000.0000.0080.0000.0080.0060.0000.0000.0000.0060.0060.0070.0040.0080.0070.0060.0000.0000.0001.000

Missing values

2023-01-05T11:29:40.096552image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-01-05T11:29:40.829244image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-01-05T11:29:41.802924image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

IDncap ratingis power door lockspolicyholder ageis parking camerarear brakes typeis adjustable steeringis tpmsis driver seat height adjustablesegmentcar ageis central lockingis rear window wiperheightcluster areais ecwfuel typetorqueengine volumetransmission typemanufacturercylinderis rear window washeris front fog lightsis brake assistis power steeringis escpopulationis rear window defoggertime periodengine typeis speed alertsteering typelengthwidthis parking sensorspowergross weightis day night rear view mirrormodelgear boxairbagsturning radiusarea danger levelis claim
002.0Yes0.394496NoDrumNoNoNoB10.08YesNo1675.0Area_8YesCNG82.1Nm@3400rpm998.0Manual1.03.0NoNoNoYesNo8883.0No0.304554K10CYesPower3655.01620.0Yes55.92bhp@5300rpm1340.0NoModel_85.02.04.740
112.0Yes0.417364NoDrumNoNoNoB10.06YesNo1675.0Area_5YesCNG82.1Nm@3400rpm998.0Manual1.03.0NoNoNoYesNo34751.0No1.169197K10CYesPower3655.01620.0Yes55.92bhp@5300rpm1340.0NoModel_85.02.04.711
222.0Yes0.438922NoDrumYesNoYesB20.03YesNo1530.0Area_2YesPetrol113Nm@4400rpm1197.0Manual1.04.0NoYesYesYesNo27026.0No1.219797K Series Dual jetYesElectric3845.01735.0Yes88.50bhp@6000rpm1335.0YesModel_65.02.04.850
330.0No0.656276NoDrumNoNoNoA0.04NoNo1475.0Area_13NoCNG60Nm@3500rpm796.0Manual1.03.0NoNoNoYesNo5482.0No0.746151F8D Petrol EngineYesPower3445.01515.0Yes40.36bhp@6000rpm1185.0NoModel_15.02.04.620
443.0Yes0.462328YesDiscYesYesYesC20.04YesYes1635.0Area_9YesDiesel250Nm@2750rpm1493.0Automatic3.04.0YesYesYesYesYes17833.0Yes0.1719811.5 L U2 CRDiYesPower4300.01790.0Yes113.45bhp@4000rpm1720.0NoModel_46.06.05.230
550.0No0.593278NoDrumNoNoNoA0.03NoNo1475.0Area_3NoCNG60Nm@3500rpm796.0Manual1.03.0NoNoNoYesNo4087.0No0.414677F8D Petrol EngineYesPower3445.01515.0Yes40.36bhp@6000rpm1185.0NoModel_15.02.04.650
662.0Yes0.597060NoDrumYesNoYesB20.04YesNo1530.0Area_6YesPetrol113Nm@4400rpm1197.0Manual1.04.0NoYesYesYesNo13132.0No1.123627K Series Dual jetYesElectric3845.01735.0Yes88.50bhp@6000rpm1335.0YesModel_65.02.04.830
770.0No0.549770NoDrumNoNoNoA0.02NoNo1475.0Area_3NoCNG60Nm@3500rpm796.0Manual1.03.0NoNoNoYesNo4138.0No0.236393F8D Petrol EngineYesPower3445.01515.0Yes40.36bhp@6000rpm1185.0NoModel_15.02.04.610
880.0No0.403229NoDrumNoNoNoA0.03NoNo1475.0Area_2NoCNG60Nm@3500rpm796.0Manual1.03.0NoNoNoYesNo27030.0No1.250164F8D Petrol EngineYesPower3445.01515.0Yes40.36bhp@6000rpm1185.0NoModel_15.02.04.610
992.0Yes0.833121NoDrumNoNoNoB10.06YesNo1675.0Area_14YesCNG82.1Nm@3400rpm998.0Manual1.03.0NoNaNNoYesNo7814.0No1.164330K10CYesPower3655.01620.0Yes55.92bhp@5300rpm1340.0NoModel_85.02.04.730
IDncap ratingis power door lockspolicyholder ageis parking camerarear brakes typeis adjustable steeringis tpmsis driver seat height adjustablesegmentcar ageis central lockingis rear window wiperheightcluster areais ecwfuel typetorqueengine volumetransmission typemanufacturercylinderis rear window washeris front fog lightsis brake assistis power steeringis escpopulationis rear window defoggertime periodengine typeis speed alertsteering typelengthwidthis parking sensorspowergross weightis day night rear view mirrormodelgear boxairbagsturning radiusarea danger levelis claim
43582435823.0Yes0.727984YesDiscYesYesYesC20.06YesYes1635.0Area_2YesDiesel250Nm@2750rpm1493.0Automatic3.04.0YesYesYesYesYes27089.0Yes0.6911571.5 L U2 CRDiYesPower4300.01790.0Yes113.45bhp@4000rpm1720.0NoModel_46.06.05.210
43583435830.0No0.465426NoDrumNoNoNoA0.01NoNo1475.0Area_1NoCNG60Nm@3500rpm796.0Manual1.03.0NoNoNoYesNo5025.0No0.856750F8D Petrol EngineYesPower3445.01515.0Yes40.36bhp@6000rpm1185.0NoModel_15.02.04.620
43584435842.0Yes0.580489NoDrumYesNoYesB20.08YesNo1530.0Area_2YesPetrol113Nm@4400rpm1197.0Manual1.04.0NoYesYesYesNo27046.0No0.189539K Series Dual jetYesElectric3845.01735.0Yes88.50bhp@6000rpm1335.0YesModel_65.02.04.840
43585435850.0No0.405692NoDrumNaNNoNoA0.01NoNo1475.0Area_4NoCNG60Nm@3500rpm796.0Manual1.03.0NoNoNoYesNo21666.0No0.150154F8D Petrol EngineYesPower3445.01515.0Yes40.36bhp@6000rpm1185.0NoModel_15.02.04.610
43586435862.0Yes0.636400NoDrumNoNoNoB10.08YesNo1675.0Area_3YesCNG82.1Nm@3400rpm998.0Manual1.03.0NoNoNoYesNo4172.0No0.134393K10CYesPower3655.01620.0Yes55.92bhp@5300rpm1340.0NoModel_85.02.04.710
43587435872.0Yes0.644798NoDrumYesNoYesB20.05YesNo1530.0Area_8YesPetrol113Nm@4400rpm1197.0Manual1.04.0NoYesYesYesNo8828.0No0.125440K Series Dual jetYesElectric3845.01735.0Yes88.50bhp@6000rpm1335.0YesModel_65.02.04.820
43588435882.0Yes0.674452YesDrumYesNoYesC10.11YesNo1515.0Area_13YesPetrol113Nm@4400rpm1197.0Automatic1.04.0NoYesYesYesYes5455.0Yes1.1776181.2 L K12N DualjetYesElectric3995.01735.0Yes88.50bhp@6000rpm1335.0YesModel_25.02.04.810
43589435892.0Yes0.869782NoDrumYesNoYesB20.15YesNo1530.0Area_2YesPetrol113Nm@4400rpm1197.0Manual1.04.0NoYesYesYesNo27082.0No0.601047K Series Dual jetYesElectric3845.01735.0Yes88.50bhp@6000rpm1335.0YesModel_65.02.04.830
43590435902.0Yes0.559554NoDrumYesNoYesB20.14YesNo1530.0Area_8YesPetrol113Nm@4400rpm1197.0Manual1.04.0NoYesYesYesNo8845.0No0.442004K Series Dual jetYesElectric3845.01735.0Yes88.50bhp@6000rpm1335.0YesModel_65.02.04.810
43591435910.0No0.630818NoDrumNoNoNoA0.01NoNo1475.0Area_2NoCNG60Nm@3500rpm796.0Manual1.03.0NoNoNoYesNo27052.0No0.813746F8D Petrol EngineYesPower3445.01515.0Yes40.36bhp@6000rpm1185.0NoModel_15.02.04.610